Morphologically rich languages accentuate two properties of distributional
vector space models: 1) the difficulty of inducing accurate representations for
low-frequency word forms; and 2) insensitivity to distinct lexical relations
that have similar distributional signatures. These effects are detrimental for
language understanding systems, which may infer that 'inexpensive' is a
rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In
this work, we propose a novel morph-fitting procedure which moves past the use
of curated semantic lexicons for improving distributional vector spaces.
Instead, our method injects morphological constraints generated using simple
language-specific rules, pulling inflectional forms of the same word close
together and pushing derivational antonyms far apart. In intrinsic evaluation
over four languages, we show that our approach: 1) improves low-frequency word
estimates; and 2) boosts the semantic quality of the entire word vector
collection. Finally, we show that morph-fitted vectors yield large gains in the
downstream task of dialogue state tracking, highlighting the importance of
morphology for tackling long-tail phenomena in language understanding tasks.

提出了一种新颖的基于词形的约束方法，通过使用简单的语言特定规则来改进分布向量空间的语义质量，这种方法可以提高低频词估计的准确性，并在解决语言理解任务的长尾现象方面取得显著的效果。